计算机应用与软件2025，Vol.42Issue(4)：135-141,149,8.DOI:10.3969/j.issn.1000-386x.2025.04.021
基于改进CenterNet的轻量级目标检测算法
LIGHTWEIGHT OBJECT DETECTION ALGORITHM BASED ON IMPROVED CENTERNET
摘要
Abstract
Aimed at the problem that the CenterNet detection algorithm has a large number of network parameters and fails to fully and effectively utilize the multi-scale local region features,an MIR-SPPA-CenterNet detection method is proposed to improve the CenterNet detection network.Specifically,mixed invert residual(MIR)block was introduced into the backbone network of CenterNet to achieve a lightweight effect.In addition,an improved spatial pyramid pooling with attention(SPPA)block was introduced to pool,cascade,and filter multi-scale local area features so that the network could adaptively learn more comprehensive and effective target features.Experiments show that this method has better detection results on the general PASCAL VOC dataset and the self-built L-KITTI dataset.关键词
目标检测/轻量化/CenterNetKey words
Detection/Lightweight/CenterNet分类
信息技术与安全科学引用本文复制引用
倪一华,闫胜业..基于改进CenterNet的轻量级目标检测算法[J].计算机应用与软件,2025,42(4):135-141,149,8.基金项目
国家自然科学基金项目(61300163). （61300163）
